The landscape of fear has profound effects on the species behavior, with most organisms engaging in risk avoidance behaviors in areas perceived as riskier. Most risk avoidance behaviors, such as temporal avoidance, have severe trade-offs between foraging efficiency and risk reduction. Human activities are able to affect the species landscape of fear, by increasing mortality of individuals (i.e. hunting, roadkill) and by disruption of the clues used by the species to estimate predation risk (e.g. light pollution). In this study, we used an extensive camera-trapping and night-time light satellite imagery to evaluate whether human activities affect the diel activity patterns of 17 species of rainforest dwelling mammals. We found evidence of diel activity shifts in eight of 17 analyzed species, in which five species become 21.6 % more nocturnal and three species become 11.7% more diurnal in high disturbed areas. This activity shifts were observed for both diurnal and nocturnal species. Persecuted species (game and predators) were more susceptible to present activity shifts. Since changes in foraging activity may affect species fitness, the behavior of humans’ avoidance may be another driver of the Anthropocene defaunation.

Modelling techniques for the propagation of light pollution in the atmosphere allow the computation of maps of artificial night sky brightness in any direction of the sky, involving a large number of details from satellite data. Cinzano et al. (2001a) introduced a method of mapping naked eye star visibility at the zenith from large areas based on satellite radiance measurements and Garstang models of the propagation of light pollution. It takes into account the altitude of each land area from digital elevation data, natural sky brightness in the chosen sky direction based on the Garstang approach, eye capability after Garstang and Schaefer, and atmospheric extinction in the visual photometric band. Here we discuss how to use these methods to obtain maps of the average number of visible stars when looking at the night sky hemisphere, finally answering, site by site, the question of how many stars are visible in the sky. This is not trivial, as the number of stars visible depends on the limiting magnitude in each direction in the sky, and this depends on sky brightness in that direction, atmospheric extinction at that zenith distance and the observer's visual acuity and experience. We present, as an example, a map of the number of visible stars in Italy to an average observer on clear nights with a resolution of approximately 1 km.

Scattering by aerosols and gases cause a certain fraction of artificial light emitted upwards is redirected to the ground. Of all atmospheric constituents just the aerosols are most important modulators of night-sky brightness under cloudless conditions. Unlike most of the previous we highlight a crucial role of solar radiometry for determining the atmospheric optical depth before night-time observation is to be made. Aerosol optical depth at visible wavelengths extracted from the data measured provides then the information on size distribution or mean refractive index of aerosol particles that in turn are both necessary to make night sky brightness prediction more accurate. Therefore, combining daytime and night-time radiometry we can achieve accuracy much higher than ever before. This is due to significantly reduced uncertainty in aerosol properties.

The aerosol data are retrieved from a new portable multi-wavelength optical analyzer that operates Ocean Optics spectrometer. The equipment provides the radiance data from 350 nm to 1000 nm with spectral resolution of 1 nm. Due to high sun radiance levels we use a system of mirrors each reducing the signal to about 4%, while keeping the integration time short. The minimum integration time of 3 ms allows for detection of direct sunlight. The system developed is sensitive to small changes in the aerosol system, while showing a good detection limit even under low turbidity conditions. The system performance is demonstrated in field experiment conducted shortly after front passage when most of aerosol particles is effectively removed by rain.

Night sky brightness over Montsec Observatory (north-east of Spain) has been computed and checked against measurements using Illumina numerical model [2]. In a previous publication [20] the methodology was validated and light pollution received in the observatory coming from a unique city was computed. Here we present a simulation that includes all the sources that has a significant impact over the quality of the night sky in this area. The decision of which sources should be included in the simulations was taken following the methodology explained by [6]: using a point spread function (PSF) as a simple approach to estimate which sources are brightening the sky dome over the observer. An ad hoc PSF derived with Illumina was used with the purpose of avoiding to have to rely on already existing empirical PSF. The resulting PSF can be used in any location with similar atmospheric conditions. Differences in the spectrum of the lamps can be accounted easily by adjusting a spectrum scale factor. Illumina simulates the artificial sky brightness received (W/sr/m2) by an observer from any direction. Adding the natural sky brightness allows to compare the simulations to measurements taken with different instrumentation. In our case simulations were checked against ASTMON, SQC and SQM measurements. They show a good agreement both in absolute values and in geographical patterns for the three filters studied, B, V and R. The methodology presented opens many possibilities, such as increasing the reliability of the maps that point out the light pollution main contributors for any location, and reducing the amount of time needed to perform an accurate simulation of the night sky brightness.

Clouds are a kind of atmospheric factor that most effectively scatters the artificial light coming from the ground. Therefore, they have the most significant impact on the brightness of the night sky. The paper analyses the influence of both the level of cloudiness, as well as the genera of clouds and altitude of its base, on amplifying of the light pollution. The impact of cloudiness on the brightness of the night sky in places with different levels of light pollution was researched. Measurements of meteorological elements were used together with clouds genera assessments. The introduction of an innovative method of identifying some genera of clouds on the base of the all-night continuous measurements of the sky's brightness allowed for a similar analysis in the absence of observational data specifying the genera of clouds.

A linear correlation between the cloudiness and the brightness of the night sky was found. The determined linear correlation parameters allow for specifying the three types of light-polluted areas, possibly related to the density of population. It was found that among the nine genera of the identified night clouds, the Altocumulus, Cirrocumulus, and Cumulonimbus ones are responsible for this correlation. No dependence of the brightness of the night sky on the clouds’ albedo was found. In case of overcast sky, there was a clear relationship between the average altitude of the individual genus of clouds and the brightness of the night sky. Most of the night sky brightness comes from the light scattered on the lowest altitude clouds genera, while the least contribution comes from the light scattered on the high-level clouds. It was also found that at the freezing temperatures, the layer of aerosols forms below the level of the genera Nimbostratus or Stratus. This layer, thickening with the decreasing temperature, additionally scatters the artificial light.